AWS Machine Learning Blog
Announcing specialized support for extracting data from invoices and receipts using Amazon Textract
Receipts and invoices are documents that are critical to small and medium businesses (SMBs), startups, and enterprises for managing their accounts payable processes. These types of documents are difficult to process at scale because they follow no set design rules, yet any individual customer encounters thousands of distinct types of these documents. In this post, […]
Detect small shapes and objects within your images using Amazon Rekognition Custom Labels
There are multiple scenarios in which you may want to use computer vision to detect small objects or symbols within a given image. Whether it’s detecting company logos on grocery store shelves to manage inventory, detecting informative symbols on documents, or evaluating survey or quiz documents that contain checkmarks or shaded circles, the size ratio […]
Bring your own container to project model accuracy drift with Amazon SageMaker Model Monitor
The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or […]
Detect defects and augment predictions using Amazon Lookout for Vision and Amazon A2I
With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy […]
Automate annotation of image training data with Amazon Rekognition
Every machine learning (ML) model demands data to train it. If your model isn’t predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process—second only to data cleaning. What data cleaning looks like varies from dataset to dataset. For example, the following is […]
Simplify patient care with a custom voice assistant using Amazon Lex V2
For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is stored in the hospital electronic health […]
TC Energy builds an intelligent document processing workflow to process over 20 million images with Amazon AI
This is a guest post authored by Paul Ngo, US Gas Technical and Operational Services Data Team Lead at TC Energy. TC Energy operates a network of pipelines, including 57,900 miles of natural gas and 3,000 miles of oil and liquid pipelines, throughout North America. TC Energy enables a stable network of natural gas and […]
Simplify data annotation and model training tasks with Amazon Rekognition Custom Labels
For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human intelligence. However, manual dataset annotation can […]
Smart city traffic anomaly detection using Amazon Lookout for Metrics and Amazon Kinesis Data Analytics Studio
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Cities across the world are transforming their public services infrastructure with the mission of enhancing the quality of life of its residents. Roads and traffic management systems […]
Use Amazon SageMaker Feature Store in a Java environment
Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their own feature management system. Additionally, you […]